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An approximate dynamic programming approach for sequential pig marketing decisions at herd level

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  • Pourmoayed, Reza
  • Nielsen, Lars Relund

Abstract

One of the most important operations in the production of growing/finishing pigs is the marketing of pigs for slaughter. While pork production can be managed at different levels (animal, pen, section, or herd), it is beneficial to consider the herd level when determining the optimal marketing policy due to inter-dependencies, such as those created by fixed transportation costs and cross-level constraints.

Suggested Citation

  • Pourmoayed, Reza & Nielsen, Lars Relund, 2019. "An approximate dynamic programming approach for sequential pig marketing decisions at herd level," European Journal of Operational Research, Elsevier, vol. 276(3), pages 1056-1070.
  • Handle: RePEc:eee:ejores:v:276:y:2019:i:3:p:1056-1070
    DOI: 10.1016/j.ejor.2019.01.050
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    References listed on IDEAS

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    1. Ben-Ari, Yaron & Gal, Shmuel, 1986. "Optimal replacement policy for multicomponent systems: An application to a dairy herd," European Journal of Operational Research, Elsevier, vol. 23(2), pages 213-221, February.
    2. Kathryn A. Boys & Ning Li & Paul V. Preckel & Allan P. Schinckel & Kenneth A. Foster, 2007. "Economic Replacement of a Heterogeneous Herd," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(1), pages 24-35.
    3. Pourmoayed, Reza & Nielsen, Lars Relund & Kristensen, Anders Ringgaard, 2016. "A hierarchical Markov decision process modeling feeding and marketing decisions of growing pigs," European Journal of Operational Research, Elsevier, vol. 250(3), pages 925-938.
    4. Shmuel Gal, 1989. "The Parameter Iteration Method in Dynamic Programming," Management Science, INFORMS, vol. 35(6), pages 675-684, June.
    5. D. P. de Farias & B. Van Roy, 2003. "The Linear Programming Approach to Approximate Dynamic Programming," Operations Research, INFORMS, vol. 51(6), pages 850-865, December.
    6. Warren B. Powell, 2010. "Feature Article ---Merging AI and OR to Solve High-Dimensional Stochastic Optimization Problems Using Approximate Dynamic Programming," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 2-17, February.
    7. Toft, Nils & Kristensen, Anders R. & Jorgensen, Erik, 2005. "A framework for decision support related to infectious diseases in slaughter pig fattening units," Agricultural Systems, Elsevier, vol. 85(2), pages 120-137, August.
    8. Kristensen, Anders Ringgaard, 1992. "Optimal replacement in the dairy herd: A multi-component system," Agricultural Systems, Elsevier, vol. 39(1), pages 1-24.
    9. Huseyin Topaloglu & Warren B. Powell, 2006. "Dynamic-Programming Approximations for Stochastic Time-Staged Integer Multicommodity-Flow Problems," INFORMS Journal on Computing, INFORMS, vol. 18(1), pages 31-42, February.
    10. Jeffrey Ohlmann & Philip Jones, 2011. "An integer programming model for optimal pork marketing," Annals of Operations Research, Springer, vol. 190(1), pages 271-287, October.
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